




import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil






@pytest.fixture
def    snake_case(							__magic_name__       )					->				Any:





     '''simple docstring'''


     lowercase   :  Dict        =					tmp_path / '''file.csv'''
     lowercase   :  List[str]        =					textwrap.dedent(
         '''\\n        header1,header2\n        1,2\n        10,20\n        '''       )
     with open(_A					,						'''w'''       ) as f:
          f.write(_A       )
     return str(_A       )






@pytest.fixture
def    snake_case(							__magic_name__       )					->				str:





     '''simple docstring'''


     lowercase   :  Union[str, Any]        =					tmp_path / '''malformed_file.csv'''
     lowercase   :  str        =					textwrap.dedent(
         '''\\n        header1,header2\n        1,2\n        10,20,\n        '''       )
     with open(_A					,						'''w'''       ) as f:
          f.write(_A       )
     return str(_A       )






@pytest.fixture
def    snake_case(							__magic_name__					,						__magic_name__       )					->				List[Any]:





     '''simple docstring'''


     lowercase   :  Optional[Any]        =					tmp_path / '''csv_with_image.csv'''
     lowercase   :  Optional[Any]        =					textwrap.dedent(
         F"""\
        image
        {image_file}
        """       )
     with open(_A					,						'''w'''       ) as f:
          f.write(_A       )
     return str(_A       )






@pytest.fixture
def    snake_case(							__magic_name__       )					->				Union[str, Any]:





     '''simple docstring'''


     lowercase   :  Optional[Any]        =					tmp_path / '''csv_with_label.csv'''
     lowercase   :  Union[str, Any]        =					textwrap.dedent(
         '''\\n        label\n        good\n        bad\n        good\n        '''       )
     with open(_A					,						'''w'''       ) as f:
          f.write(_A       )
     return str(_A       )






@pytest.fixture
def    snake_case(							__magic_name__       )					->				str:





     '''simple docstring'''


     lowercase   :  Any        =					tmp_path / '''csv_with_int_list.csv'''
     lowercase   :  Dict        =					textwrap.dedent(
         '''\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        '''       )
     with open(_A					,						'''w'''       ) as f:
          f.write(_A       )
     return str(_A       )






def    snake_case(							__magic_name__					,						__magic_name__					,						__magic_name__       )					->				List[Any]:





     '''simple docstring'''


     lowercase   :  Optional[int]        =					Csv()
     lowercase   :  str        =					csv._generate_tables([[csv_file, malformed_csv_file]]       )
     with pytest.raises(_A					,						match='''Error tokenizing data'''       ):
          for _ in generator:
               pass
     assert any(
         record.levelname == '''ERROR'''
         and '''Failed to read file''' in record.message
         and os.path.basename(_A       ) in record.message
         for record in caplog.records       )






@require_pil
def    snake_case(							__magic_name__       )					->				Any:





     '''simple docstring'''


     with open(_A					,						encoding='''utf-8'''       ) as f:
          lowercase   :  Tuple        =					f.read().splitlines()[1]
     lowercase   :  str        =					Csv(encoding='''utf-8'''					,						features=Features({'''image''': Image()}       )       )
     lowercase   :  List[Any]        =					csv._generate_tables([[csv_file_with_image]]       )
     lowercase   :  Tuple        =					pa.concat_tables([table for _, table in generator]       )
     assert pa_table.schema.field('''image'''       ).type == Image()()
     lowercase   :  Union[str, Any]        =					pa_table.to_pydict()['''image''']
     assert generated_content == [{"path": image_file, "bytes": None}]






def    snake_case(							__magic_name__       )					->				Optional[int]:





     '''simple docstring'''


     with open(_A					,						encoding='''utf-8'''       ) as f:
          lowercase   :  Any        =					f.read().splitlines()[1:]
     lowercase   :  Optional[int]        =					Csv(encoding='''utf-8'''					,						features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']       )}       )       )
     lowercase   :  Dict        =					csv._generate_tables([[csv_file_with_label]]       )
     lowercase   :  List[str]        =					pa.concat_tables([table for _, table in generator]       )
     assert pa_table.schema.field('''label'''       ).type == ClassLabel(names=['''good''', '''bad''']       )()
     lowercase   :  Dict        =					pa_table.to_pydict()['''label''']
     assert generated_content == [ClassLabel(names=['''good''', '''bad''']       ).straint(_A       ) for label in labels]






def    snake_case(							__magic_name__       )					->				Any:





     '''simple docstring'''


     lowercase   :  Optional[int]        =					Csv(encoding='''utf-8'''					,						sep=''','''					,						converters={'''int_list''': lambda __magic_name__       : [int(_A       ) for i in x.split()]}       )
     lowercase   :  Any        =					csv._generate_tables([[csv_file_with_int_list]]       )
     lowercase   :  Optional[int]        =					pa.concat_tables([table for _, table in generator]       )
     assert pa.types.is_list(pa_table.schema.field('''int_list'''       ).type       )
     lowercase   :  Any        =					pa_table.to_pydict()['''int_list''']
     assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase     		=							{
    'configuration_xlm_roberta_xl': [
        'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaXLConfig',
        'XLMRobertaXLOnnxConfig',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase     		=							[
						    'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'XLMRobertaXLForCausalLM',
						    'XLMRobertaXLForMaskedLM',
						    'XLMRobertaXLForMultipleChoice',
						    'XLMRobertaXLForQuestionAnswering',
						    'XLMRobertaXLForSequenceClassification',
						    'XLMRobertaXLForTokenClassification',
						    'XLMRobertaXLModel',
						    'XLMRobertaXLPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    XLMRobertaXLConfig,
						    XLMRobertaXLOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_roberta_xl import (
												    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMRobertaXLForCausalLM,
												    XLMRobertaXLForMaskedLM,
												    XLMRobertaXLForMultipleChoice,
												    XLMRobertaXLForQuestionAnswering,
												    XLMRobertaXLForSequenceClassification,
												    XLMRobertaXLForTokenClassification,
												    XLMRobertaXLModel,
												    XLMRobertaXLPreTrainedModel,
												)

else:
						import sys

						__UpperCamelCase     		=							_LazyModule(__name__, globals()['__file__'], _import_structure)


